Target recognition based on stratified synthesis strategy

Yao, Dangnai; Chai, Hua; Wang, Zhenguo

doi:10.2991/jimet-15.2015.62

Cited by 1 publication

(1 citation statement)

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“…Fuzzy comprehensive can fuse information from different features to reduce the impact of uncertainty and improve the reliability of recognition. However, traditional static fuzzy comprehensive [27][28][29] tends to ignore the correlation and complementarity of information at previous and subsequent moments and does not fully consider the spatio-temporal relationship between variables with different features and different moments, leading to errors in inference results.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Dimensional Feature Fusion Recognition Method for Space Infrared Dim Targets Based on Fuzzy Comprehensive with Spatio-Temporal Correlation

Zhang,

Rao,

et al. 2024

Remote Sensing

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Space infrared (IR) target recognition has always been a key issue in the field of space technology. The imaging distance is long, the target is weak, and the feature discrimination is low, making it difficult to distinguish between high-threat targets and decoys. However, most existing methods ignore the fuzziness of multi-dimensional features, and their performance mainly depends on the accuracy of feature extraction, with certain limitations in handling uncertainty and noise. This article proposes a space IR dim target fusion recognition method, which is based on fuzzy comprehensive of spatio-temporal correlation. First, we obtained multi-dimensional IR features of the target through multi-time and multi-spectral detectors, then we established and calculated the adaptive fuzzy-membership function of the features. Next, we applied the entropy weight method to ascertain the objective fusion weights of each feature and computed the spatially fuzzified fusion judgments for the targets. Finally, the fuzzy comprehensive function was used to perform temporal recursive judgment, and the ultimate fusion recognition result was obtained by integrating the results of each temporal recursive judgment. The simulation and comparative experimental results indicate that the proposed method improved the accuracy and robustness of IR dim target recognition in complex environments. Under ideal conditions, it can achieve an accuracy of 88.0% and a recall of 97.5% for the real target. In addition, this article also analyzes the impact of fusion feature combinations, fusion frame counts, different feature extraction errors, and feature database size on recognition performance. The research in this article can enable space-based IR detection systems to make more accurate and stable decisions, promoting defense capabilities and ensuring space security.

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Section: Introductionmentioning

confidence: 99%